NFL Analytics: Exploring personnel groupings

library(tidyverse)
library(nflverse)
library(gt)

nfl_analytics_theme <- function(..., base_size = 12) {
  
  theme(
    text = element_text(family = "Roboto",
                        size = base_size,
                        color = "black"),
    axis.ticks = element_blank(),
    axis.title = element_text(face = "bold"),
    axis.text = element_text(face = "bold"),
    plot.title.position = "plot",
    plot.title = element_markdown(size = 16,
                                  vjust = .02,
                                  hjust = 0.5),
    plot.subtitle = element_markdown(hjust = 0.5),
    plot.caption = element_markdown(size = 8),
    panel.grid.minor = element_blank(),
    panel.grid.major =  element_line(color = "#d0d0d0"),
    panel.background = element_rect(fill = "#f7f7f7"),
    plot.background = element_rect(fill = "#f7f7f7"),
    panel.border = element_blank(),
    legend.background = element_rect(color = "#F7F7F7"),
    legend.key = element_rect(color = "#F7F7F7"),
    legend.title = element_text(face = "bold"),
    legend.title.align = 0.5,
    strip.text = element_text(face = "bold"))
}

teams <- nflreadr::load_teams()

Snap counts

I’d like to replicate that Pickens was used in 59% of snaps against the Cowboys in Week 5.

snap_counts <- load_snap_counts(seasons = most_recent_season())

snap_counts <- snap_counts %>% 
  left_join(x=.,
            y=teams,
            by=c('team' = 'team_abbr'))

snap_counts

── nflverse snap counts ────────────────────────────────────────────────────────────────────────────────────────────────
ℹ Data updated: 2024-10-15 08:40:40 EDT

Roman Wilson

snap_counts %>% 
  filter(player == 'Roman Wilson')

── nflverse snap counts ────────────────────────────────────────────────────────────────────────────────────────────────
ℹ Data updated: 2024-10-15 08:40:40 EDT

Personnel groupings in 2024

(personnel <- nflreadr::load_participation(seasons = 2023, include_pbp = TRUE))

── nflverse play-by-play participation ─────────────────────────────────────────────────────────────────────────────────
ℹ Data updated: 2024-02-28 02:32:38 MST

personnel %>% 
  filter(team == 'PIT')

Error in `filter()`:
ℹ In argument: `team == "PIT"`.
Caused by error:
! object 'team' not found
Backtrace:
 1. personnel %>% filter(team == "PIT")
 3. dplyr:::filter.data.frame(., team == "PIT")
 4. dplyr:::filter_rows(.data, dots, by)
 5. dplyr:::filter_eval(...)
 7. mask$eval_all_filter(dots, env_filter)
 8. dplyr (local) eval()

Analysis of the three-edge look (Watt, Highsmith, Herbig)

From Congelio book

(participation <- nflreadr::load_participation(season = 2024, include_pbp = TRUE))

(participation_split <- participation %>%
  filter(!is.na(offense_formation)) %>%
  filter(posteam == "PIT") %>%
  tidyr::separate_rows(offense_players, sep = ";") %>%
  group_by(offense_personnel, offense_players) %>%
  summarize(total = n(),
            .groups = 'drop'))

roster_2023 <- nflreadr::load_rosters(2023) %>%
  select(full_name, gsis_id, headshot_url)

participation_split <- participation_split %>%
  left_join(roster_2023, by = c("offense_players" = "gsis_id"))
participation_split